def update(self):
'''
Update the income process, the assets grid, and the terminal solution.
Parameters
----------
none
Returns
-------
none
'''
orig_flow = self.time_flow
if self.cycles == 0: # hacky fix for labor supply l_bar
self.updateIncomeProcessAlt()
else:
self.updateIncomeProcess()
self.updateAssetsGrid()
self.updateSolutionTerminal()
self.timeFwd()
self.resetRNG()
if self.cycles > 0:
self.IncomeDstn = Model.applyFlatIncomeTax(
self.IncomeDstn,
tax_rate=self.tax_rate,
T_retire=self.T_retire,
unemployed_indices=range(0, (self.TranShkCount + 1) *
self.PermShkCount,
self.TranShkCount + 1))
self.makeIncShkHist()
if not orig_flow:
self.timeRev()
Params.init_consumer_objects[
"PermGroFacAgg"] = 1.0 # Aggregate permanent income growth factor
Params.init_consumer_objects[
"aNrmInitMean"] = -10.0 # Mean of log initial assets
Params.init_consumer_objects[
"aNrmInitStd"] = 1.0 # Standard deviation of log initial assets
Params.init_consumer_objects[
"pLvlInitMean"] = 0.0 # Mean of log initial permanent income
Params.init_consumer_objects[
"pLvlInitStd"] = 0.0 # Standard deviation of log initial permanent income
Params.init_consumer_objects[
"CubicBool"] = False # Not available in the bequest model
# %%
# Make an instance of a lifecycle consumer to be used for estimation
LifeCyclePop = cShksModl.IndShockConsumerType(**Params.init_consumer_objects)
# %% {"code_folding": [0]}
# Solve and simulate the model (ignore the "warning" message)
LifeCyclePop.solve() # Obtain consumption rules by age
LifeCyclePop.unpackcFunc() # Expose the consumption rules
# Which variables do we want to track
LifeCyclePop.track_vars = [
'aNrmNow', 'pLvlNow', 'mNrmNow', 'cNrmNow', 'TranShkNow', 't_cycle'
]
LifeCyclePop.T_sim = 120 # Nobody lives to be older than 145 years (=25+120)
LifeCyclePop.initializeSim(
) # Construct the age-25 distribution of income and assets
LifeCyclePop.simulate(
params["UnempPrb"] = 0.05
params["IncUnemp"] = 0.3
params["UnempPrbRet"] = 0.0
params["IncUnempRet"] = 0.0
params.update({
"BoroCnstArt": 0.0,
"vFuncBool": False,
"CubicBool": False,
"aXtraMin": 0.001,
"aXtraMax": 50.0,
"aXtraNestFac": 3,
"aXtraCount": 48,
"aXtraExtra": [None],
})
example = ConsIndShockModel.IndShockConsumerType(**params)
else:
params = copy.deepcopy(params)
params.pop("PermShkStd", None)
params.pop("TranShkStd", None)
example = ConsIndShockModel.PerfForesightConsumerType(**params)
examples.append(example)
results.append(st.empty())
for i, example in enumerate(examples):
example.solve()
results[i].markdown(
f"**Model {1+i}** agent's human wealth is {example.solution[0].hNrm:.02f} times "
f"his current income level, with its consumption function is defined (consumption is "
f"positive) down to m_t = {example.solution[0].mNrmMin:.02f}.")
import HARK.ConsumptionSaving.ConsIndShockModel as Model # Consumption-saving model with idiosyncratic shocks
from HARK.utilities import plotFuncs, plotFuncsDer # Basic plotting tools
from time import clock # Timing utility
from copy import deepcopy # "Deep" copying for complex objects
from HARK.parallel import multiThreadCommandsFake, multiThreadCommands # Parallel processing
mystr = lambda number: "{:.4f}".format(number) # Format numbers as strings
import numpy as np # Numeric Python
if __name__ == '__main__': # Parallel calls *must* be inside a call to __main__
type_count = 32 # Number of values of CRRA to solve
# Make the basic type that we'll use as a template.
# The basic type has an artificially dense assets grid, as the problem to be
# solved must be sufficiently large for multithreading to be faster than
# single-threading (looping), due to overhead.
BasicType = Model.IndShockConsumerType(**Params.init_idiosyncratic_shocks)
BasicType.cycles = 0
BasicType(aXtraMax=100, aXtraCount=64)
BasicType(vFuncBool=False, CubicBool=True)
BasicType.updateAssetsGrid()
BasicType.timeFwd()
# Solve the basic type and plot the results, to make sure things are working
start_time = clock()
BasicType.solve()
end_time = clock()
print('Solving the basic consumer took ' + mystr(end_time - start_time) +
' seconds.')
BasicType.unpackcFunc()
print('Consumption function:')
plotFuncs(BasicType.cFunc[0], 0, 5) # plot consumption
# Make agent live for sure until the terminal period pf_dict['T_retire'] = t_cycle pf_dict['LivPrb'] = [1] * t_cycle # Shut down income growth pf_dict['PermGroFac'] = [1] * t_cycle # Decrease grid for speed pf_dict['aXtraCount'] = 100 # %% Create both agents port_agent = cpm.PortfolioConsumerType(**pf_dict) port_agent.solve() pf_agent = cis.PerfForesightConsumerType(**pf_dict) pf_agent.solve() # %% Construct the analytical solution rho = pf_dict['CRRA'] R = pf_dict['Rfree'] Beta = pf_dict['DiscFac'] T = pf_dict['T_cycle'] + 1 thorn_r = (R * Beta)**(1 / rho) / R ht = lambda t: (1 - (1 / R)**(T - t + 1)) / (1 - 1 / R) - 1 kappa = lambda t: (1 - thorn_r) / (1 - thorn_r**(T - t + 1)) true_cFunc = lambda t, m: np.minimum(m, kappa(t) * (m + ht(t)))
